{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:13:15Z","timestamp":1787616795372,"version":"build-2736575974"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aim<\/jats:title>\n                    <jats:p>Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (GITB) is challenging. We aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for this purpose.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The AI model was developed and assessed using an internal dataset comprising 1,132 colonoscopy images of CD and 1,045 colonoscopy images of GITB at a tertiary referral center. Its stand\u2010alone performance was further evaluated in an external dataset comprising 67 colonoscopy images of 17 CD patients and 63 colonoscopy images of 14 GITB patients from other institutions. Additionally, a crossover trial involving three expert endoscopists and three trainee endoscopists compared AI\u2010assisted and unassisted human interpretations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the internal dataset, the sensitivity, specificity, and accuracy of the AI model in distinguishing between CD and GITB were 95.3%, 100.0%, and 97.7%, respectively, with an area under the ROC curve of 0.997. In the external dataset, the AI model exhibited a sensitivity, specificity, and accuracy of 77.8%, 85.1%, and 81.5%, respectively, with an area under the ROC curve of 0.877. In the human endoscopist trial, AI assistance increased the pooled accuracy of the six endoscopists from 86.2% to 88.8% (\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.010). While AI did not significantly enhance diagnostic accuracy for the experts (96.7% with AI\n                      <jats:italic>vs<\/jats:italic>\n                      95.6% without,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.360), it significantly improved accuracy for the trainees (81.0%\n                      <jats:italic>vs<\/jats:italic>\n                      76.7%,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0=\u00a00.002).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This AI model shows potential in aiding the accurate differential diagnosis between CD and GITB, particularly benefiting less experienced endoscopists.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgh.16788","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T20:54:27Z","timestamp":1730753667000},"page":"115-122","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Artificial intelligence\u2010aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis"],"prefix":"10.1111","volume":"40","author":[{"given":"Kwangbeom","family":"Park","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Nowon Eulji Medical Center Eulji University  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jisup","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Convergence Medicine, Asan Medical Center University of Ulsan College of Medicine  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung 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